How far is the era of artificial intelligence for continuous renal replacement therapy?_West China Medical Journal

Authors：

LIU Tong ^1,2 ,  BAI Ming ¹ ,  SUN Shiren ¹

1. Department of Nephrology, the First Affiliated Hospital of Air Force Medical University, Xi’an, Shaanxi 710032, P. R. China;
2. Medical School of Yan’an University, Yan’an, Shaanxi 716000, P. R. China;

Corresponding?author：

BAI Ming, Email: mingbai1983@126.com; SUN Shiren, Email: sunshiren@medmail.com.cn

Keywords：

Continuous renal replacement therapy; artificial intelligence; acute kidney injury

DOI：

10.7507/1002-0179.202406096

Video：

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Continuous renal replacement therapy (CRRT) is one of the important therapeutic techniques for critically ill patients. In recent years, the field of artificial intelligence has developed rapidly and has been widely applied in manufacturing, automotive, and even daily life. The development and application of artificial intelligence in the medical field are also advancing rapidly, and artificial intelligence radiographic imaging result judgment, pathological result judgment, patient prognosis prediction are gradually being used in clinical practice. The development of artificial intelligence in the field of CRRT has also made rapid progress. Therefore, this article will elaborate on the current application status of artificial intelligence in CRRT, as well as its future prospects in CRRT, so as to provide a reference for understanding the application of artificial intelligence in CRRT.

Citation： LIU Tong, BAI Ming, SUN Shiren. How far is the era of artificial intelligence for continuous renal replacement therapy?. West China Medical Journal, 2024, 39(7): 1019-1022. doi: 10.7507/1002-0179.202406096 Copy

1.	陳香美. 血液凈化標準操作規程. 北京: 人民衛生出版社, 2021.
2.	Samoni S, Husain-Syed F, Villa G, et al. Continuous renal replacement therapy in the critically ill patient: from garage technology to artificial intelligence. J Clin Med, 2021, 11(1): 172.
3.	Kidney Disease: Improving Global Outcomes (KDIGO) Acute Kidney Injury Work Group. KDIGO clinical practice guideline for acute kidney injury. Kidney Int Suppl, 2012, 2(1): 1-138.
4.	Hoste EA, Bagshaw SM, Bellomo R, et al. Epidemiology of acute kidney injury in critically ill patients: the multinational AKI-EPI study. Intensive Care Med, 2015, 41(8): 1411-1423.
5.	Luo X, Jiang L, Du B, et al. A comparison of different diagnostic criteria of acute kidney injury in critically ill patients. Crit Care, 2014, 18(4): R144.
6.	沈文瑋. 論當代人工智能的技術特點及其對勞動者的影響. 當代經濟研究, 2018(4): 63-69.
7.	余乃忠. 理解為自然歷史過程的人工智能. 中州學刊, 2020(10): 122-129.
8.	全耀. 淺談人工智能的發展史. 現代信息科技, 2019, 3(6): 80-81, 84.
9.	Wald R, Bagshaw SM, STARRT-AKI Investigators. Timing of initiation of renal-replacement therapy in acute kidney injury. Reply. N Engl J Med, 2020, 383(18): 1797-1798.
10.	Zarbock A, Kellum JA, Schmidt C, et al. Effect of early vs delayed initiation of renal replacement therapy on mortality in critically ill patients with acute kidney injury: the ELAIN randomized clinical trial. JAMA, 2016, 315(20): 2190-2199.
11.	Guru PK, Singh TD, Passe M, et al. Derivation and validation of a search algorithm to retrospectively identify CRRT initiation in the ECMO patients. Appl Clin Inform, 2016, 7(2): 596-603.
12.	Roy S, Mincu D, Loreaux E, et al. Multitask prediction of organ dysfunction in the intensive care unit using sequential subnetwork routing. J Am Med Inform Assoc, 2021, 28(9): 1936-1946.
13.	張婭峰. 基于機器學習的 ICU 連續腎臟替代治療干預預測模型研究. 廣州: 華南理工大學, 2020.
14.	Kang MW, Kim S, Kim YC, et al. Machine learning model to predict hypotension after starting continuous renal replacement therapy. Sci Rep, 2021, 11(1): 17169.
15.	肖桂林. 基于機器學習算法預測行連續性腎臟替代治療的急性腎損傷患者死亡率. 南昌: 南昌大學醫學部, 2023.
16.	Yoo KD, Noh J, Bae W, et al. Predicting outcomes of continuous renal replacement therapy using body composition monitoring: a deep-learning approach. Sci Rep, 2023, 13(1): 4605.
17.	Suppadungsuk S, Thongprayoon C, Miao J, et al. Exploring the potential of chatbots in critical care nephrology. Medicines (Basel), 2023, 10(10): 58.
18.	Zhang L, Baldwin I, Zhu G, et al. Automated electronic monitoring of circuit pressures during continuous renal replacement therapy: a technical report. Crit Care Resusc, 2015, 17(1): 51-54.
19.	唐雪, 李森淼, 張凌, 等. 連續性腎臟替代治療護理信息化系統的構建及應用. 中國血液凈化, 2022, 21(4): 300-304.
20.	Chen H, Ma Y, Hong N, et al. Early warning of citric acid overdose and timely adjustment of regional citrate anticoagulation based on machine learning methods. BMC Med Inform Decis Mak, 2021, 21(Suppl 2): 126.
21.	李墨奇, 伍薇, 何文昌, 等. 構建急性腎損傷患者連續性腎臟替代治療劑量達成模型. 中國衛生質量管理, 2022, 29(1): 74-81,90.
22.	鄭潔皎, 高文. 數字醫療帶給老年康復的挑戰. 華西醫學, 2023, 38(6): 810-814.
23.	姚鵬, 唐時元, 蔣耀文, 等. 人工智能在急診醫學中的應用現狀與展望. 華西醫學, 2022, 37(11): 1601-1606.
24.	Hammouda N, Neyra JA. Can artificial intelligence assist in delivering continuous renal replacement therapy?. Adv Chronic Kidney Dis, 2022, 29(5): 439-449.
25.	Liu LJ, Takeuchi T, Chen J, et al. Artificial intelligence in continuous kidney replacement therapy. Clin J Am Soc Nephrol, 2023, 18(5): 671-674.

1. 陳香美. 血液凈化標準操作規程. 北京: 人民衛生出版社, 2021.
2. Samoni S, Husain-Syed F, Villa G, et al. Continuous renal replacement therapy in the critically ill patient: from garage technology to artificial intelligence. J Clin Med, 2021, 11(1): 172.
3. Kidney Disease: Improving Global Outcomes (KDIGO) Acute Kidney Injury Work Group. KDIGO clinical practice guideline for acute kidney injury. Kidney Int Suppl, 2012, 2(1): 1-138.
4. Hoste EA, Bagshaw SM, Bellomo R, et al. Epidemiology of acute kidney injury in critically ill patients: the multinational AKI-EPI study. Intensive Care Med, 2015, 41(8): 1411-1423.
5. Luo X, Jiang L, Du B, et al. A comparison of different diagnostic criteria of acute kidney injury in critically ill patients. Crit Care, 2014, 18(4): R144.
6. 沈文瑋. 論當代人工智能的技術特點及其對勞動者的影響. 當代經濟研究, 2018(4): 63-69.
7. 余乃忠. 理解為自然歷史過程的人工智能. 中州學刊, 2020(10): 122-129.
8. 全耀. 淺談人工智能的發展史. 現代信息科技, 2019, 3(6): 80-81, 84.
9. Wald R, Bagshaw SM, STARRT-AKI Investigators. Timing of initiation of renal-replacement therapy in acute kidney injury. Reply. N Engl J Med, 2020, 383(18): 1797-1798.
10. Zarbock A, Kellum JA, Schmidt C, et al. Effect of early vs delayed initiation of renal replacement therapy on mortality in critically ill patients with acute kidney injury: the ELAIN randomized clinical trial. JAMA, 2016, 315(20): 2190-2199.
11. Guru PK, Singh TD, Passe M, et al. Derivation and validation of a search algorithm to retrospectively identify CRRT initiation in the ECMO patients. Appl Clin Inform, 2016, 7(2): 596-603.
12. Roy S, Mincu D, Loreaux E, et al. Multitask prediction of organ dysfunction in the intensive care unit using sequential subnetwork routing. J Am Med Inform Assoc, 2021, 28(9): 1936-1946.
13. 張婭峰. 基于機器學習的 ICU 連續腎臟替代治療干預預測模型研究. 廣州: 華南理工大學, 2020.
14. Kang MW, Kim S, Kim YC, et al. Machine learning model to predict hypotension after starting continuous renal replacement therapy. Sci Rep, 2021, 11(1): 17169.
15. 肖桂林. 基于機器學習算法預測行連續性腎臟替代治療的急性腎損傷患者死亡率. 南昌: 南昌大學醫學部, 2023.
16. Yoo KD, Noh J, Bae W, et al. Predicting outcomes of continuous renal replacement therapy using body composition monitoring: a deep-learning approach. Sci Rep, 2023, 13(1): 4605.
17. Suppadungsuk S, Thongprayoon C, Miao J, et al. Exploring the potential of chatbots in critical care nephrology. Medicines (Basel), 2023, 10(10): 58.
18. Zhang L, Baldwin I, Zhu G, et al. Automated electronic monitoring of circuit pressures during continuous renal replacement therapy: a technical report. Crit Care Resusc, 2015, 17(1): 51-54.
19. 唐雪, 李森淼, 張凌, 等. 連續性腎臟替代治療護理信息化系統的構建及應用. 中國血液凈化, 2022, 21(4): 300-304.
20. Chen H, Ma Y, Hong N, et al. Early warning of citric acid overdose and timely adjustment of regional citrate anticoagulation based on machine learning methods. BMC Med Inform Decis Mak, 2021, 21(Suppl 2): 126.
21. 李墨奇, 伍薇, 何文昌, 等. 構建急性腎損傷患者連續性腎臟替代治療劑量達成模型. 中國衛生質量管理, 2022, 29(1): 74-81,90.
22. 鄭潔皎, 高文. 數字醫療帶給老年康復的挑戰. 華西醫學, 2023, 38(6): 810-814.
23. 姚鵬, 唐時元, 蔣耀文, 等. 人工智能在急診醫學中的應用現狀與展望. 華西醫學, 2022, 37(11): 1601-1606.
24. Hammouda N, Neyra JA. Can artificial intelligence assist in delivering continuous renal replacement therapy?. Adv Chronic Kidney Dis, 2022, 29(5): 439-449.
25. Liu LJ, Takeuchi T, Chen J, et al. Artificial intelligence in continuous kidney replacement therapy. Clin J Am Soc Nephrol, 2023, 18(5): 671-674.

West China Medical Journal

How far is the era of artificial intelligence for continuous renal replacement therapy?

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